Access to this full-text is provided by MDPI.
Content available from Nutrients
This content is subject to copyright.
Citation: Della Corte, K.; Jalo, E.;
Kaartinen, N.E.; Simpson, L.; Taylor,
M.A.; Muirhead, R.; Raben, A.;
Macdonald, I.A.; Fogelholm, M.;
Brand-Miller, J. Longitudinal
Associations of Dietary Sugars and
Glycaemic Index with Indices of
Glucose Metabolism and Body
Fatness during 3-Year Weight Loss
Maintenance: A PREVIEW
Sub-Study. Nutrients 2023,15, 2083.
https://doi.org/10.3390/
nu15092083
Academic Editor: Laura Chiavaroli
Received: 14 March 2023
Revised: 21 April 2023
Accepted: 22 April 2023
Published: 26 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
nutrients
Article
Longitudinal Associations of Dietary Sugars and Glycaemic
Index with Indices of Glucose Metabolism and Body Fatness
during 3-Year Weight Loss Maintenance: A PREVIEW
Sub-Study
Karen Della Corte 1, Elli Jalo 2, Niina E. Kaartinen 3, Liz Simpson 4, Moira A. Taylor 4, Roslyn Muirhead 1,
Anne Raben 5,6, Ian A. Macdonald 7, Mikael Fogelholm 2and Jennie Brand-Miller 1, *,†
1School of Life and Environmental Sciences and Charles Perkins Centre, University of Sydney,
Sydney, NSW 2006, Australia; karendellacorte@gmail.com (K.D.C.); roslyn.muirhead@sydney.edu.au (R.M.)
2Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland; elli.jalo@helsinki.fi (E.J.)
3Department of Public Health and Welfare, Finnish Institute for Health and Welfare, 00271 Helsinki, Finland;
niina.kaartinen@thl.fi
4Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, Queen’s Medical Centre,
National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre,
Nottingham NG7 2RD, UK; liz.simpson@nottingham.ac.uk (L.S.); moira.taylor@nottingham.ac.uk (M.A.T.)
5Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen,
1958 Copenhagen, Denmark; ara@nexs.ku.dk
6Clinical Research, Copenhagen University Hospital–Steno Diabetes Center Copenhagen,
2730 Herlev, Denmark
7Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham NG7 2RD, UK
*Correspondence: jennie.brandmiller@sydney.edu.au; Tel.: +61-417-658-695
† Current address: D17-Charles Perkins Centre Research and Education Hub, The University of Sydney,
Sydney, NSW 2006, Australia.
Abstract:
Background: Dietary sugars are often linked to the development of overweight and type 2
diabetes (T2D) but inconsistencies remain. Objective: We investigated associations of added, free,
and total sugars, and glycaemic index (GI) with indices of glucose metabolism (IGM) and indices
of body fatness (IBF) during a 3-year weight loss maintenance intervention. Design: The PREVIEW
(PREVention of diabetes through lifestyle Intervention and population studies in Europe and around
the World) study was a randomised controlled trial designed to test the effects of four diet and
physical activity interventions, after an 8-week weight-loss period, on the incidence of T2D. This
secondary observational analysis included pooled data assessed at baseline (8), 26, 52, 104 and
156 weeks from 514 participants with overweight/obesity (age 25–70 year; BMI
≥
25 kg
·
m
−2
) and
with/without prediabetes in centres that provided data on added sugars (Sydney and Helsinki)
or free sugars (Nottingham). Linear mixed models with repeated measures were applied for IBF
(total body fat, BMI, waist circumference) and for IGM (fasting insulin, HbA1c, fasting glucose,
C-peptide). Model A was adjusted for age and intervention centre and Model B additionally adjusted
for energy, protein, fibre, and saturated fat. Results: Total sugars were inversely associated with
fasting insulin and C-peptide in all centres, and free sugars were inversely associated with fasting
glucose and HbA1c (Model B: all p< 0.05). Positive associations were observed between GI and IGM
(Model B: fasting insulin, HbA1c, and C-peptide: (all p< 0.01), but not for added sugars. Added
sugar was positively associated with body fat percentage and BMI, and GI was associated with waist
circumference (Model B: all p< 0.01), while free sugars showed no associations (Model B: p> 0.05).
Conclusions: Our findings suggest that added sugars and GI were independently associated with 3-y
weight regain, but only GI was associated with 3-y changes in glucose metabolism in individuals at
high risk of T2D.
Keywords:
dietary sugar; added sugar; glycaemic index; glucose metabolism; type 2 diabetes;
overweight; body weight
Nutrients 2023,15, 2083. https://doi.org/10.3390/nu15092083 https://www.mdpi.com/journal/nutrients
Nutrients 2023,15, 2083 2 of 14
1. Introduction
In parallel with obesity, type 2 diabetes (T2D) represents a major public health concern,
with the incidence and/or prevalence of comorbidities continually increasing [
1
,
2
]. To
date, modifiable risk factors for T2D include sugar-sweetened beverage (SSB) intake [
3
,
4
],
but evidence for other sources of dietary sugars is scant [
5
]. The glycaemic index (GI),
a measure of the postprandial impact of dietary carbohydrates, has a more consistent
positive association with the risk of T2D than dietary sugars [
6
]. In contrast, dietary
sugars (more specifically SSB), have been identified as key contributors to overweight and
obesity [
7
,
8
]. Nonetheless, the development of dietary guidelines has been hindered by
inconsistent findings arising from differences in amounts, sources, and forms of sugar as
well as inadequate study design. Sugars consumed in liquid form may be less satiating
than in solid forms [
9
], although total meal replacements in the form of liquid ‘shakes’ (50%
energy as sugars) have been successfully used in rapid weight loss interventions. In ad
libitum and excess energy settings, added sugars appear to promote weight gain but not
when exchanged for starches or other carbohydrates [10–12].
Although T2D is a disease of abnormal carbohydrate metabolism, meta-analyses
from prospective cohort studies have shown no clear association between the sum of all
digestible carbohydrates and incident T2D [
13
–
15
]. In contrast, epidemiological findings
support the concept that the structure (physical and chemical) of carbohydrate-rich foods
rather than their quantity has the strongest effect on health outcomes [
6
,
16
,
17
]. Reducing
postprandial glycaemia may be more relevant to lowering the risk for T2D by reducing
beta-cell demand [
18
]. In observational studies, higher energy-adjusted GI and total
glycaemic load (GL) were found to be independent risk factors for T2D [
19
]. In relatively
short randomised controlled trials, lower GI diets resulted in improvements in glucose
homeostasis in some studies [20,21], but not in others [22].
The PREVIEW (PREVention of diabetes through lifestyle Intervention and population
studies in Europe and around the World) study was a long-term randomised controlled
weight loss maintenance trial designed to test the effects of four diet and physical activity
interventions in a 2
×
2 factorial design on the incidence of T2D in individuals with
overweight or obesity and pre-diabetes after an initial 8-week weight loss period on a
predominately liquid low-energy diet [
23
]. In post hoc analyses of PREVIEW, dietary
GI and GL were positively associated with weight regain and deteriorating glycaemic
status [
24
]. The aim of the present secondary analysis was to investigate longitudinal
associations of added, free and total sugars intakes [
25
], as well as GI as a comparator,
with indices of glucose metabolism (IGM) (fasting plasma glucose, insulin, C-peptide and
HbA
1c
) and indices of body fat (IBF) (total body fat, waist circumference, and BMI) during a
3-year weight loss maintenance phase in those centres that provided data on added and/or
free sugars.
2. Participants and Methods
2.1. Study Design
This study was a secondary observational analysis of the PREVIEW Study, a large
3-year, multinational, randomised intervention trial [
23
]. The original study was assessed
in males and females aged 25–70 year, with overweight (BMI
≥
25 kg/m
2
) and pre-diabetes.
The study comprised a weight-loss phase followed by a weight-loss maintenance (WLM)
phase [
23
]. Briefly, during the first 8-weeks, participants adhered to a low-energy diet
(800 kcal/d)
with the aim to lose
≥
8% of initial body weight. Those who met the target
and were T2D-free or had prediabetes after weight loss were eligible for the WLM phase.
In this stage (duration 148 weeks), the effects of a healthy high-protein low-GI diet (HPLG)
[protein 25% of energy intake (en%), carbohydrate 45 en%, GI
≤
50] were compared with a
healthy moderate-protein moderate-GI (MPMG) diet (protein 15 en%, carbohydrate 55 en%,
GI
≥
56) in combination with one of two exercise regimens (high-intensity or moderate-
intensity exercise) [
23
]. The main results were published previously [
26
]. The current
sub-study is an observational analysis based on data from the WLM phase
(8 to 156 weeks),
Nutrients 2023,15, 2083 3 of 14
irrespective of original randomisation. The start of WLM (at 8 weeks) was considered the
baseline for this analysis.
Only participants from the Sydney, Helsinki, and Nottingham cohorts of PREVIEW
could be included because they had data on added or free sugars either from the national
food composition tables or through calculation. Data were collected between August
2013 and March 2018. Overweight and obesity were defined as a BMI of 25.0–29.9 kg/m
2
and
≥
30.0 kg/m
2
, respectively. Prediabetes and T2D were evaluated in accordance with
American Diabetes Association criteria [27].
Self-reported dietary intakes were assessed at 26, 52, 104, and 156 weeks. Dietary data
at 26-weeks were used to estimate the diet at 8 weeks. Reported dietary intake was assessed
using 4-day weighed dietary records which included four consecutive days including one
weekend day. Participants were instructed how to use scales and conventional household
measurements and to record in detail all foods and beverages consumed. Food records were
reviewed by research dietitians during the clinical investigation days to assess the adequacy
of the information. Research dietitians entered the individual food records by means of
proprietary software in use in each centre (FoodWorks Professional, AivoDiet, Nutritics).
The GI of each food was obtained by use of GI databases. For mixed meals and some
recipes, the weighted mean GI of the meal components was calculated [
28
] as previously
described by Louie et al. [
29
]. Total GI and GL were calculated according to the formula of
van Woudenbergh et al. [
30
]. Depending on national databases, added and free sugars were
calculated within each intervention centre based on total and natural sugars amounts [
29
,
31
].
“Total sugars” refer to all monosaccharides (glucose, fructose, galactose) and disaccharides
(sucrose, maltose, lactose), including lactose present in milk products and sugars contained
within the cellular structure of foods (e.g., whole fruits). “Added sugars” were defined as
sugars added to foods during processing, manufacturing, or home preparation (including
honey, molasses, fruit juice concentrate, brown sugar, corn sweetener, sucrose, lactose,
glucose, high-fructose corn syrup, malt syrup). “Free sugars” also included sugars naturally
present in unsweetened fruit juices. The intervention centres of Sydney and Helsinki
reported on added sugars and Nottingham reported on free sugars.
Information on age, sex, stature and ethnicity was collected with self-administered
questionnaires at week 0. On every clinical investigation day, body weight was measured
when participants were in a fasting state, with an empty bladder and wearing light clothing
or underwear. BMI was calculated as body weight in kilograms divided by square height
in meters. Fat mass was obtained using GE Lunar prodigy (eCORE 2005 software version
9.30.044) at Nottingham, Hologic Discovery (APEX software version 4) at Sydney and
bioelectrical impedance (InBod 720 Body Composition Analyzer 2004; Biospace Co., Ltd.,
Seoul, Korea) at Helsinki. Waist circumference was measured when participants were at
the end of breath expiration, at the midway point between the bottom of the rib cage and
the top of the iliac crest. All staff were trained in the standard operating procedures at
joint training sessions. All study participants provided written informed consent prior to
commencing screening measurements. The study was approved at all intervention centres
by the local human research ethics committees and was conducted in accordance with
the latest revision of the Declaration of Helsinki (59th WMA General Assembly, Seoul,
Republic of Korea, October 2008). University of Sydney: HREC approval on 24 June
2013, then subsequent approval by Sydney Local Health District Human Ethics Research
Committee (SLHD HREC) on 8 April 2015 (Protocol No is X14-0408), which overrode the
HREC approval. University of Helsinki: Coordinating Ethical Committee of HUS (Helsinki
and Uusimaa Hospital District); 18 June 2013. University of Nottingham: UK National
Research Ethics Service (NRES); 13/EM/0259; 5 July 2013.
The main outcomes of interest were fasting plasma glucose, insulin, and C-peptide
concentrations, and glycated hemoglobin A
1c
(HbA
1c
) measured at each time point. The sec-
ondary outcomes of interest were fat mass, waist circumference, and BMI. Information on
outcomes of interest was assessed at 8, 26, 52, 104 and 156 weeks. Fasting (>10 h) blood sam-
ples were initially stored at
−
80
◦
C at the intervention sites and transported to the accredited
Nutrients 2023,15, 2083 4 of 14
laboratory (Finnish Institute for Health and Welfare, Helsinki, Finland) to be analysed with
an Architect ci8200 integrated system (Abbott Laboratories, Abbott Park, IL, USA).
2.2. Statistical Analysis
Characteristics of the study population are presented as mean
±
SD or median (25th,
75th percentile) for continuous variables and as absolute (relative) frequencies for categori-
cal variables and presented in Supplementary Material.
Participants from the Universities of Sydney and Helsinki were merged into one
group for assessment of longitudinal associations of added and total sugars, and GI with
changes in markers of glycaemic status and body fatness during the WLM phase. Data
from the University of Nottingham were analysed separately because data on free sugars,
not added sugars, were provided. Data were not pooled for all three centres because direct
comparisons between added/free sugars to total sugars and GI were required. To achieve
normal distribution in outcome variables log
e
or square root transformations were used.
Dietary variables (total, added and free sugars, GI, protein, total fat, saturated fat, starch,
and fibre intakes) were energy-adjusted by the residual method to provide a measure of
these nutrient intakes uncorrelated with energy intake [32].
Prospective associations between dietary sugars intake (total sugars, added sugars,
free sugars) or glycaemic index and risk markers of type 2 diabetes (fasting insulin, fasting
glucose, HbA
1c
, C-peptide) and indices of body fatness (body fat percentage, waist circum-
ference, BMI) were analysed by the use of linear mixed models with repeated measures.
The initial regression model (model A) included the predictors of energy-adjusted sugars
intake (total, free, added) or GI as well as age and intervention centre. Adjusted models
(model B) were built on model A and were constructed by individual examination of
potential influencing covariates and inclusion of those which substantially modified the
predictor–outcome associations (
≥
10%) or significantly predicted the outcome. Potential
confounding covariates considered were (1) other dietary factors [intakes of total protein,
total fat, saturated fat, trans fat, starch, GI, fibre, energy], (2) anthropometric factors [fat
mass (%), BMI, waist circumference, hip circumference, body weight], (3) other factors [age,
sex, ethnicity (Caucasian, Asian, African, Arabic, Hispanic, or other)]. The final model B
included model A (including age and intervention centre) and additionally adjusted for
the intakes of energy, protein, fibre, and saturated fat as well as body fat as fixed effects
for glucose metabolism outcomes. These models were additionally adjusted for time as
a fixed effect and intervention centre and participant identifier as random effects and an
autoregressive model was applied for the covariance matrix. To retain comparability of
results, models were adjusted identically for variables of glucose metabolism and body
fatness and the building of the models was conducted for the primary exposures.
For easier interpretation, results from the linear mixed models with repeated measure-
ments are presented as adjusted least-square means (95% CI) by tertiles of the respective
predictor, while p-values stem from models with the predictors as continuous variables.
Hence, the linear mixed models included the energy-adjusted predictors as grouped tertiles
versus continuous variables in order for adjusted least-square means to be calculated and
presented. The linear mixed models with repeated measurements included up to five time
points per participant, thus allowing for variability over time to be accounted for in sum-
marised results. Data were analysed under the assumption that missing data were missing
at random. Both complete-case and available-case analyses were included in this study.
A sensitivity analysis was conducted at 26 weeks, which excluded dropout participants
whose last investigation visit was at 6 months (n= 20/20 of dropouts for added/free sugars
groups). SAS statistical software package version 9.2 (SAS Institute Inc., Cary, NC, USA)
was used for all statistical analyses. Statistical significance was set at a p-value < 0.05.
3. Results
A total of 709 subjects entered the WLM phase at the three sites. Of these, 514 had
available GI, dietary sugars, and main outcome information; 132 from the University of
Nutrients 2023,15, 2083 5 of 14
Sydney, 211 from the University of Helsinki, and 171 from the University of Nottingham.
Characteristics of the participants at baseline are presented in Supplementary Table S1. In
the Sydney/Helsinki group (70.0% females), the median baseline BMI kg/m
2
was 29.0
(range 26.1–33.1), and mean total and added sugars intakes (
±
SD) were 17.5%
±
4.6 and
4.2%
±
2.9 of total energy, respectively. For the Nottingham group (56.1% female), the
median baseline BMI kg/m
2
was 29.8 (range 27.2–33.6), and mean total and free sugars
intakes (
±
SD) were 20.1%
±
6.7 and 5.6%
±
3.6 of total energy, respectively. Results for the
sensitivity analyses produced similar findings as the main results.
3.1. Indices of Glucose Metabolism
Added sugars intake was not associated with fasting insulin, fasting glucose, HbA
1c
or
C-peptide in the adjusted model B (all p> 0.05; see Figure 1and Table 1). Free sugars intake
was inversely associated with fasting plasma glucose (p= 0.008) and HbA
1c
(p= 0.035) (see
Figure 2and Supplementary Table S2). Total sugars intake was inversely associated with
fasting insulin, fasting glucose and C-peptide in the Sydney and Helsinki groups (Model B:
all p< 0.05; see Figure 1, Table 1) and with fasting insulin and C-peptide in the Nottingham
group (see Supplementary Table S2). The intake of fruit as a source of naturally-occurring
sugar showed protective associations with IGM (see Supplementary Table S3).
Nutrients 2023, 15, x FOR PEER REVIEW 7 of 15
Figure 1. Plasma levels of fasting insulin, glucose, C-peptide, and HbA1c (glycated hemoglobin), as
well as body fat, BMI (body mass index) and waist circumference by tertiles of added sugar, total
sugar and glycaemic index for the combined Sydney and Helsinki intervention groups (n = 343).
Data are generic least square means and 95% CI for model A (adjusted for age and intervention
centre) and model B (additionally adjusted for intakes of energy, protein, fibre, and saturated fat).
Indices of glucose metabolism were also adjusted for body fat.
Figure 1.
Plasma levels of fasting insulin, glucose, C-peptide, and HbA1c (glycated hemoglobin),
as well as body fat, BMI (body mass index) and waist circumference by tertiles of added sugar, total
Nutrients 2023,15, 2083 6 of 14
sugar and glycaemic index for the combined Sydney and Helsinki intervention groups (n= 343). Data
are generic least square means and 95% CI for model A (adjusted for age and intervention centre)
and model B (additionally adjusted for intakes of energy, protein, fibre, and saturated fat). Indices of
glucose metabolism were also adjusted for body fat.
Table 1.
Associations of added sugar, total sugar, and glycemic index with indices of glucose
metabolism for Sydney and Helsinki intervention groups (n= 343).
Tertiles of Added Sugar Tertiles of Total Sugar Tertiles of Glycemic Index
Low
(T1) Moderate
(T2) High (T3) Ptrend Low
(T1) Moderate
(T2)
High
(T3) Ptrend Low
(T1) Moderate
(T2)
High
(T3) Ptrend
Intake (g/d)
or GI a5 (4; 6) 15 (14; 16) 33 (33; 35) 47 (46;
48) 70 (69; 71)
99 (97;
100) 48 (46;
49) 55 (53;
56) 62 (60;
63)
Fasting insulin (pmol/L)
Model A
8.60
(8.20;
9.01)
8.97
(8.57;
9.38)
9.17
(8.77;
9.58)
0.002 9.21
(8.83;
9.61)
8.98
(8.59;
9.37)
8.45
(8.00;
8.89)
0.009 8.63
(8.19;
9.10)
8.73
(8.29;
9.17)
9.29
(8.87;
9.70)
0.010
Model B
8.86
(7.99;
9.74)
8.85
(7.98;
9.72)
8.80
(7.92;
9.67)
0.746
9.05
(8.17;
9.94)
8.97
(8.08;
9.86)
8.39
(7.47;
9.30)
0.046 8.52
(7.93;
9.12)
8.65
(8.07;
9.22)
9.32
(8.73;
9.92)
0.003
HbA1c (mmol/mol)
Model A
36.4
(35.7;
37.1)
36.5
(35.8;
37.1)
36.9
(36.2;
37.6)
0.019 36.8
(36.2;
37.4)
36.4
(35.8;
37.0)
36.6
(36.0;
37.2)
0.155
36.1
(35.1;
37.1)
36.8
(35.8;
37.8)
36.9
(35.9;
37.9)
<0.001
Model B
36.5
(35.9;
37.0)
36.4
(36.2;
37.0)
36.8
(36.2;
37.3)
0.218
36.7
(36.2;
37.2)
36.4
(35.9;
36.9)
36.7
(36.1;
37.2)
0.566
36.1
(35.3;
36.9)
36.8
(36.0;
37.6)
36.9
(36.0;
37.7)
<0.001
Fasting glucose (mmol/L)
Model A
6.02
(5.87;
6.18)
6.01
(5.86;
6.16)
6.01
(5.86;
6.16)
0.954
6.10
(5.94;
6.25)
5.96
(5.81;
6.11)
5.97
(5.82;
6.12)
<0.001 5.96
(5.84;
6.09)
6.01
(5.93;
6.18)
6.03
(5.90;
6.15) 0.209
Model B
6.03
(5.85;
6.21)
6.01
(5.83;
6.18)
5.99
(5.82;
6.17)
0.527
6.07
(5.89;
6.26)
5.96
(5.78;
6.14)
5.99
(5.80;
6.17)
0.024 5.96
(5.82;
6.12)
6.04
(5.89;
6.19)
6.03
(5.87;
6.18) 0.131
C-peptide
(pmol/L)
Model A
719
(650;
788)
731
(663; 801) 744
(674; 813) 0.001 746
(681;
811)
735
(670; 800)
706
(640;
772)
0.038 703
(615;
791)
741
(654;
829)
751
(663;
838)
0.006
Model B
736
(713;
759)
720
(699; 742) 717
(694; 740)
0.909
738
(717;
760)
732
(711; 754)
698
(673;
722)
0.045 702
(665;
740)
736
(700;
771)
740
(703;
777)
0.005
Values are adjusted least-square means (95% CIs) unless otherwise indicated. Linear trends (P
trend
) were obtained
using a linear mixed model with repeated measures. The predictor of glycemic index as well as the transformed
and energy-adjusted predictors of dietary added sugar intake and total sugar intake were used as continuous
variables. Model A adjusted for age at time of study begin and intervention center. Model B additionally adjusted
for body fat percentage, energy intake, protein intake, fibre intake and saturated fat intake. Transformations of
variables for analysis: log
e
for protein intake, saturated fat intake, energy intake, HbA1c, insulin and C-peptide;
square root for total and added sugar intakes. HbA1c: glycated hemoglobin A1c.
a
Values are unadjusted medians
(25th, 75th percentile). p-values stem from models with predictors as continuous variables. Bold values indicate
significant findings (p< 0.05).
GI was positively associated with fasting insulin (p= 0.003), HbA
1c
(p= <0.001), and
C-peptide (p= 0.005) in adjusted Model B for Sydney and Helsinki (Figure 1and Table 1).
In the Nottingham group, GI was positively associated with fasting insulin (p= 0.015) (see
Figure 2and Supplementary Table S2).
3.2. Indices of Body Fatness
Added sugars were positively associated with body fat percentage (p< 0.001) and
BMI (p= 0.006). GI was positively associated with increases in waist circumference in the
Sydney and Helsinki group (p< 0.001) (see Figures 1and 2) and in the Nottingham group
(p= 0.019) (see Supplementary Table S2). In Sydney and Helsinki, total sugars intake was
positively associated with body fat percentage (p= <0.001; Figure 1and Table 2).
Nutrients 2023,15, 2083 7 of 14
Nutrients 2023, 15, x FOR PEER REVIEW 8 of 15
Figure 2. Plasma levels of fasting insulin, glucose, C-peptide, and HbA1c (glycated hemoglobin) as
well as body fat, BMI (body mass index) and waist circumference by tertiles of added sugar, total
sugar and glycaemic index for the Noingham intervention group (n = 171). Data are generic least
square means and 95% CI for model A (adjusted for age and intervention centre) and model B (ad-
ditionally adjusted for intakes of energy, protein, fibre, and saturated fat). Indices of glucose metab-
olism were also adjusted for body fat.
3.2. Indices of Body Fatness
Added sugars were positively associated with body fat percentage (p < 0.001) and
BMI (p = 0.006). GI was positively associated with increases in waist circumference in the
Sydney and Helsinki group (p < 0.001) (see Figures 1 and 2) and in the Noingham group
(p = 0.019) (see Supplementary Table S2). In Sydney and Helsinki, total sugars intake was
positively associated with body fat percentage (p = <0.001; Figure 1 and Table 2).
Table 2. Associations of added sugar, total sugar, and glycemic index with indices of body fatness
for Sydney and Helsinki intervention groups (n = 343).
Tertiles of Added Suga
r
Tertiles of Total Suga
r
Tertiles of Glycemic Index
Low (T1)
Moderat
e
(T2)
High
(T3)
Ptren
d Low (T1)
Moderat
e
(T2)
High
(T3)
Ptre
nd Low (T1)
Moderat
e
(T2)
High
(T3) Ptrend
Intake (g/d) or
GI a 5 (4; 6) 15 (14;
16)
33 (33;
35) 47 (46;
48)
70 (69;
71)
99 (98;
100) 48 (46;
49)
55 (53;
56)
62 (60;
63)
Figure 2.
Plasma levels of fasting insulin, glucose, C-peptide, and HbA1c (glycated hemoglobin)
as well as body fat, BMI (body mass index) and waist circumference by tertiles of added sugar,
total sugar and glycaemic index for the Nottingham intervention group (n= 171). Data are generic
least square means and 95% CI for model A (adjusted for age and intervention centre) and model
B (additionally adjusted for intakes of energy, protein, fibre, and saturated fat). Indices of glucose
metabolism were also adjusted for body fat.
Table 2.
Associations of added sugar, total sugar, and glycemic index with indices of body fatness for
Sydney and Helsinki intervention groups (n= 343).
Tertiles of Added Sugar Tertiles of Total Sugar Tertiles of Glycemic Index
Low
(T1) Moderate
(T2)
High
(T3) Ptrend Low
(T1) Moderate
(T2)
High
(T3) Ptrend Low
(T1) Moderate
(T2)
High
(T3) Ptrend
Intake (g/d)
or GI a5 (4; 6) 15 (14; 16) 33 (33;
35) 47 (46;
48) 70 (69; 71) 99 (98;
100) 48 (46;
49) 55 (53; 56) 62 (60;
63)
Body fat (%)
Model A
35.7
(29.2;
42.1)
38.0
(31.6;
44.4)
38.7
(32.3;
45.1)
<0.001 36.5
(30.7;
42.4)
37.3
(31.5;
43.2)
38.5
(32.7;
44.4)
<0.001 37.0
(30.8;
43.2)
37.3
(31.1;
43.5)
37.8
(31.6;
44.0) 0.568
Model B
36.2
(29.6;
42.7)
37.7
(31.2;
44.3)
38.5
(31.9;
45.0)
<0.001 36.3
(29.8;
42.7)
37.3
(30.8;
43.7)
39.2
(32.7;
45.6)
<0.001 37.6
(31.2;
43.9)
37.4
(31.1;
43.7)
37.3
(31.0;
43.7) 0.656
Nutrients 2023,15, 2083 8 of 14
Table 2. Cont.
Tertiles of Added Sugar Tertiles of Total Sugar Tertiles of Glycemic Index
Low
(T1) Moderate
(T2)
High
(T3) Ptrend Low
(T1) Moderate
(T2)
High
(T3) Ptrend Low
(T1) Moderate
(T2)
High
(T3) Ptrend
BMI
Model A
30.0
(27.8;
32.2)
30.8
(28.6;
33.0)
31.0
(28.8;
33.2)
<0.0001 30.7
(28.7;
32.7)
30.6
(28.6;
32.6)
30.4
(28.4;
32.3) 0.278 29.7
(27.0;
32.5)
30.6
(27.9;
33.4)
31.4
(28.7;
34.1)
0.022
Model B
30.1
(27.7;
32.5)
30.8
(28.4;
33.2)
30.9
(28.5;
33.3)
0.006 30.4
(28.1;
32.8)
30.6
(28.3;
33.0)
30.8
(28.4;
33.1) 0.233 29.8
(26.9;
32.7)
30.6
(27.8;
33.5)
31.4
(28.5;
34.3) 0.142
Waist circumference (cm)
Model A
102.1
(96.6;
107.5)
102.5
(97.0;
107.9)
103.5
(98.1;
108.9)
0.024 103.8
(98.8;
109.0)
102.4
(97.3;
107.5)
101.5
(96.4;
106.6)
<0.001 100.1
(92.3;
107.8)
102.7
(94.9;
110.4)
105.5
(97.8;
113.3)
<0.0001
Model B
102.3
(96.2;
108.4)
102.7
(96.6;
108.9)
103.3
(97.2;
109.4) 0.188 103.2
(97.3;
109.2)
102.6
(96.6;
108.6)
102.4
(96.4;
108.4) 0.220 100.1
(91.9;
108.2)
102.6
(94.5;
110.7)
105.7
(97.6;
113.8)
<0.0001
Values are adjusted least-square means (95% CIs) unless otherwise indicated. Linear trends (P
trend
) were obtained
using a linear mixed model with repeated measures. The predictor of glycemic index as well as the transformed
and energy-adjusted predictors of dietary added sugar intake and total sugar intake were used as continuous
variables. Model A adjusted for age at time of study begin and intervention center. Model B additionally adjusted
for energy intake, protein intake, fibre intake and saturated fat intake. Transformations of variables for analysis:
log
e
for protein intake, saturated fat intake, energy intake, and BMI; square root for total and added sugar intakes.
BMI: body mass index.
a
Values are unadjusted medians (25th, 75th percentile). p-values stem from models with
predictors as continuous variables. Bold values indicate significant findings (p< 0.05).
4. Discussion
In this 3-year longitudinal analysis of individuals at high risk of T2D, higher added
sugars intake predicted increasing body fat but was not associated with markers of glucose
metabolism. In contrast, higher GI was associated with an increased waist circumference
and glucose intolerance in the Sydney and Helsinki group. Interestingly, these trends were
not seen with total sugars or free sugars intake, which were associated with improvements
in glucose metabolism and were not linked with IBF. This may reflect other components in
the food matrix or the type of sugar (glucose, fructose, sucrose, lactose) present.
Anecdotally, many health professionals assume that added sugars increase the risk
of developing T2D. However, average amounts of added sugars intake have either no
relationship to incident T2D [
33
–
35
], or even a beneficial association with insulin sensitivity
and fasting insulin [
36
,
37
]. Indeed, a meta-analysis of 15 prospective cohort studies reported
no association between total sugars intake and T2D, while higher sucrose consumption was
associated with decreased risk [
5
]. High amounts of sugars in liquid form, including SSBs,
have been positively associated with the risk of T2D [
3
,
38
–
43
]. However, epidemiological
investigations of all added sugars intake often showed no association [
33
,
35
,
36
,
38
,
44
–
50
].
Sugars consumed in high amounts that exceed energy needs are likely to have different
physiological effects to those consumed in energy balance [51,52].
In contrast to total carbohydrates and added sugars, many observational studies
suggest that GI is a predictor of the development of T2D [
15
,
53
–
56
], although not all
evidence is consistent [
17
,
57
]. Meta-analyses of RCTs have concluded that a low-GI diet is
more effective than a high-GI diet in improving glucose metabolism [
20
,
21
]. However, to
our knowledge, the PREVIEW study and this sub-study are the first to show an association
between dietary GI and HbA
1c
[
24
], a marker of average blood glucose concentration
over the previous 8–12 weeks, which is now used to diagnose T2D when elevated. While
the intake of carbohydrates may drive insulin resistance through a number of proposed
mechanisms, saturated fatty acid intake has also been closely tied to insulin resistance
development. In a supplementary analysis, we showed that saturated fatty acid intake was
significantly associated with fasting insulin and C-peptide (see Supplementary Table S3).
Thus, the GI is one dietary risk marker for developing insulin resistance that should be
considered alongside saturated fat intake.
In contrast to T2D, excessive consumption of sweetened drinks, and to a lesser extent
sweetened foods, is widely acknowledged to have a role in the promotion of weight
Nutrients 2023,15, 2083 9 of 14
gain in prospective and experimental studies [
58
–
64
]. Nonetheless, the relevance of these
findings to typical consumption habits has been questioned [
65
,
66
]. In a meta-analysis
of controlled feeding trials, fructose intake produced weight gain only in settings where
fructose was provided as excess energy, but not when isoenergetically exchanged for other
carbohydrates [
10
]. A second meta-analysis of RCTs reported that isoenergetic exchange of
starch for sugars had no effect on weight gain. Still, in cohort studies SSB led to significant
weight gain in those with the highest vs lowest SSB intake, suggesting that this source
may have unique effects [
11
]. If energy intake is not suppressed proportionately after
the consumption of liquid sugars [
67
–
69
], targeting SSB as a source of excess energy is
a prudent strategy. However, both dietary sugars and GI were associated with differing
indices of body fat in the current study, implying that replacing sugars with sources of high-
GI/GL starch may not be helpful. Other intervention studies have shown that consuming
a low-GI diet assists with weight control compared to other conventional diets [70].
The beneficial association between total sugars (including free sugars) and glucose
metabolism may be explained by the favourable effects of particular micronutrients and
bioactive polyphenols found within natural sources of sugars and the fact that many of
these foods have a lower GI (e.g., whole fruit, milk and dairy products). A high intake
of fruits that are naturally high in fructose is associated with good metabolic health [
66
].
Further, when the main sources of dietary fructose are fruits and vegetables in their whole
form and not as juice or smoothie, prospective studies have shown inverse associations
with the risk of incident T2D [
71
,
72
]. Indeed, fruit intake was associated with improved
glucose metabolism in our study (see Supplementary Table S3). Previous studies have
reported inverse associations between simple sugars and GI in people with diabetes [
73
],
but in the present study, the intake of added sugars was associated with a higher GI. Many
foods that are sweetened with added sucrose have shown either no association (cakes and
cookies) or a protective association (whole-grain cereals, fruit, yogurt, and even ice-cream)
with T2D [74,75].
There are notable strengths of the present study. First, we provide new evidence of the
associations between dietary sugars and health markers during longer-term weight loss
maintenance, which is more likely to address a life-long issue, particularly for individuals
with obesity and increased susceptibility to T2D. Few studies of weight loss maintenance
and weight regain have been undertaken and rarely for as long as 3 years. Secondly, unlike
some studies with fixed energy content, we determined the associations in a free-living
context and an ad libitum diet. Thirdly, because diet and health markers were measured
regularly over 3 years, we were able to leverage multiple, longitudinal, real-time data. This
large dataset allowed adjustments for multiple confounders. We investigated the role of
three types or sources of sugars (added, free and total) as well as multiple outcome indices
for both glucose metabolism and body fatness after a period of rapid weight loss. Finally,
the findings may be more generalizable because three different countries, albeit similar in
terms of economy and food culture, were represented.
Several limitations should also be noted. The GI and dietary sugars intakes were
calculated from self-reported weighed 4-day food diaries in individuals whose added
sugars intake was relatively low (4.2%
±
2.9% of total energy compared to 9.0%
±
7.5% of
total energy reported in the Australian population based on national survey data for this age
group) [
76
]. Therefore, in interpreting these results it should be noted that the relatively low
intake amounts of added and free sugars may limit the ability to observe in these data an
association with markers of glycaemia. Although these records estimate food intake more
accurately than food-frequency questionnaires, misreporting can occur [
77
]. It is possible
that sugar-rich foods were selectively underreported due to their perceived unhealthiness.
Further, low GI may be a proxy for a certain type of diet, including one which is rich in
fruit, vegetables, legumes, berries, and dairy foods. Although we have tested and adjusted
for dietary macronutrient composition, there are several dietary components (e.g., vitamins,
minerals, and polyphenols) that we could not adjust for, hence residual and unmeasured
Nutrients 2023,15, 2083 10 of 14
confounders may exist. Finally, our findings may not be relevant to individuals with normal
weight and glucose tolerance.
In conclusion, this secondary analysis of a 3-year weight loss maintenance intervention
found evidence that a higher intake of added sugars and a higher dietary GI was associated
with increased body fat or waist circumference. In contrast, only GI was an independent
predictor of worsening glucose status. Paradoxically, total and free sugar intakes were
associated with improved glucose metabolism. Taken together, the findings from this
particular study population add to the increasing body of evidence that the glycaemic
index of foods and hence fluctuations in postprandial glycaemia may be more associated
with increased future risk of T2D than added or free sugars intake.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/nu15092083/s1, Table S1: Characteristics of participants by study
population at baseline (8 weeks) and 26 weeks (diet and lifestyle outcomes); Table S2: Associations of
free sugar, total sugar, and glycemic index with indices of glucose metabolism and body fatness for
Nottingham intervention group (n= 171); Table S3: Associations of fruit intake (g/d) for Helsinki and
Nottingham (n= 383), fruit intake (serv/d) for Sydney (n= 132), and saturated fat intake for Sydney
and Helsinki and Nottingham intervention groups (n= 515).
Author Contributions:
All authors contributed to the implementation of the experimental trial and
contributed to analysis and interpretation of the data. K.D.C. was responsible for the statistical
analyses. K.D.C. and J.B.-M. analysed the data and drafted the manuscript. All authors contributed
to critical revision of the manuscript for important intellectual content. All authors have read and
agreed to the published version of the manuscript.
Funding:
The EU framework programme 7 (FP7/2007–2013) grant agreement # 312057. National
Health and Medical Research Council-EU Collaborative Grant, AUS 8, ID 1067711. The Glycaemic
Index Foundation Australia through royalties to the University of Sydney. The NZ Health Research
Council (14/191) and University of Auckland Faculty Research Development Fund. The Cambridge
Weight Plan donated all products for the 8-week LED period. The Danish Agriculture & Food Council.
The Danish Meat and Research Institute. National Institute for Health Research Biomedical Research
Centre (NIHR BRC) (UK). Biotechnology and Biological Sciences Research Council (BBSRC) (UK).
Engineering and Physical Sciences Research Council (EPSRC) (UK). Nutritics (Dublin) donated all
dietary analysis software used by UNOTT. Juho Vainio Foundation (FIN), Academy of Finland (grant
numbers: 272376, 314383, 266286, 314135), Finnish Medical Foundation, Gyllenberg Foundation, Novo
Nordisk Foundation, Finnish Diabetes Research Foundation, University of Helsinki, Government
Research Funds for Helsinki University Hospital (FIN), Jenny and Antti Wihuri Foundation (FIN),
Emil Aaltonen Foundation (FIN). The funders of the study had no role in study design, data collection,
data analysis, data interpretation or writing of the report.
Institutional Review Board Statement:
The study was conducted in accordance with the latest
revision of the Declaration of Helsinki (59th WMA General Assembly, Seoul, Republic of Korea,
October 2008). University of Sydney: HREC approval on 24 June 2013, then subsequent approval
by Sydney Local Health District Human Ethics Research Committee (SLHD HREC) on 8 April 2015
(Protocol No is X14-0408), which overrode the HREC approval. University of Helsinki: Coordinating
Ethical Committee of HUS (Helsinki and Uusimaa Hospital District); 18 June 2013. University of
Nottingham: UK National Research Ethics Service (NRES); 13/EM/0259; 5 July 2013.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
Data described in the manuscript, code book, and analytic code will be
made available upon request pending application and approval of the trial steering committee.
Acknowledgments:
The PREVIEW consortium would like to thank all study participants at every
intervention centre for their time and commitment and all scientists, advisors and students for their
dedication and contributions to the study.
Conflicts of Interest:
A.R. has received honorariums from the International Sweeteners Association,
Unilever, and Nestlé. I.A.M. was a member of the UK Government Scientific Advisory Committee on
Nutrition, Treasurer of the Federation of European Nutrition Societies, Treasurer of the World Obesity
Federation, member of the Mars Scientific Advisory Council, member of the Mars Europe Nutrition
Nutrients 2023,15, 2083 11 of 14
Advisory Board and Scientific Adviser to the Waltham Centre for Pet Nutrition, and a member of the
Novozymes Scientific Advisory Board, until March 2020. He was an employee of Nestle Research
from August 2020 until July 2022 and is now a member of the Nestle Research Scientific Advisory
Board. J.B.-M. is President and Director of the Glycaemic Index Foundation, oversees a glycaemic
index testing service at the University of Sydney and is a co-author of books about diet and diabetes.
All other authors have no conflict of interest to declare.
References
1.
Cho, N.H.; Shaw, J.; Karuranga, S.; Huang, Y.; da Rocha Fernandes, J.; Ohlrogge, A.; Malanda, B. IDF Diabetes Atlas: Global
estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res. Clin. Pract.
2018
,138, 271–281. [CrossRef]
[PubMed]
2. Chatterjee, S.; Khunti, K.; Davies, M.J. Type 2 diabetes. Lancet 2017,389, 2239–2251. [CrossRef]
3.
Malik, V.S.; Popkin, B.M.; Bray, G.A.; Després, J.-P.; Willett, W.C.; Hu, F.B. Sugar-sweetened beverages and risk of metabolic
syndrome and type 2 diabetes: A meta-analysis. Diabetes Care 2010,33, 2477–2483. [CrossRef] [PubMed]
4.
O’Connor, L.; Imamura, F.; Lentjes, M.A.; Khaw, K.-T.; Wareham, N.J.; Forouhi, N.G. Prospective associations and population
impact of sweet beverage intake and type 2 diabetes, and effects of substitutions with alternative beverages. Diabetologia
2015
,58,
1474–1483. [CrossRef] [PubMed]
5.
Tsilas, C.S.; de Souza, R.J.; Mejia, S.B.; Mirrahimi, A.; Cozma, A.I.; Jayalath, V.H.; Ha, V.; Tawfik, R.; Di Buono, M.; Jenkins, A.L.
Relation of total sugars, fructose and sucrose with incident type 2 diabetes: A systematic review and meta-analysis of prospective
cohort studies. CMAJ 2017,189, E711–E720. [CrossRef] [PubMed]
6. Livesey, G.; Taylor, R.; Livesey, H.F.; Buyken, A.E.; Jenkins, D.J.; Augustin, L.S.; Sievenpiper, J.L.; Barclay, A.W.; Liu, S.; Wolever,
T.M. Dietary glycemic index and load and the risk of type 2 diabetes: A systematic review and updated meta-analyses of
prospective cohort studies. Nutrients 2019,11, 1280. [CrossRef]
7.
Hu, F.B.; Malik, V.S. Sugar-sweetened beverages and risk of obesity and type 2 diabetes: Epidemiologic evidence. Physiol. Behav.
2010,100, 47–54. [CrossRef]
8.
Siervo, M.; Montagnese, C.; Mathers, J.C.; Soroka, K.R.; Stephan, B.C.; Wells, J.C. Sugar consumption and global prevalence of
obesity and hypertension: An ecological analysis. Public Health Nutr. 2014,17, 587–596. [CrossRef]
9.
DiMeglio, D.P.; Mattes, R.D. Liquid versus solid carbohydrate: Effects on food intake and body weight. Int. J. Obes.
2000
,24,
794–800. [CrossRef]
10.
Sievenpiper, J.L.; de Souza, R.J.; Mirrahimi, A.; Yu, M.E.; Carleton, A.J.; Beyene, J.; Chiavaroli, L.; Di Buono, M.; Jenkins, A.L.;
Leiter, L.A. Effect of fructose on body weight in controlled feeding trials: A systematic review and meta-analysis. Ann. Intern.
Med. 2012,156, 291–304. [CrossRef]
11.
Te Morenga, L.; Mallard, S.; Mann, J. Dietary sugars and body weight: Systematic review and meta-analyses of randomised
controlled trials and cohort studies. BMJ 2013,346, e7492. [CrossRef] [PubMed]
12.
Saris, W.H.; Astrup, A.; Prentice, A.M.; Zunft, H.J.; Formiguera, X.; Verboeket-van de Venne, W.P.; Raben, A.; Poppitt, S.D.;
Seppelt, B.; Johnston, S.; et al. Randomized controlled trial of changes in dietary carbohydrate/fat ratio and simple vs complex
carbohydrates on body weight and blood lipids: The CARMEN study. The Carbohydrate Ratio Management in European
National diets. Int. J. Obes. 2000,24, 1310–1318. [CrossRef] [PubMed]
13.
AlEssa, H.B.; Bhupathiraju, S.N.; Malik, V.S.; Wedick, N.M.; Campos, H.; Rosner, B.; Willett, W.C.; Hu, F.B. Carbohydrate quality
and quantity and risk of type 2 diabetes in US women. Am. J. Clin. Nutr. 2015,102, 1543–1553. [CrossRef] [PubMed]
14.
Halton, T.L.; Liu, S.; Manson, J.E.; Hu, F.B. Low-carbohydrate-diet score and risk of type 2 diabetes in women. Am. J. Clin. Nutr.
2008,87, 339–346. [CrossRef] [PubMed]
15.
Greenwood, D.C.; Threapleton, D.E.; Evans, C.E.; Cleghorn, C.L.; Nykjaer, C.; Woodhead, C.; Burley, V.J. Glycemic index, glycemic
load, carbohydrates, and type 2 diabetes: Systematic review and dose–response meta-analysis of prospective studies. Diabetes
Care 2013,36, 4166–4171. [CrossRef]
16.
Augustin, L.S.; Kendall, C.W.; Jenkins, D.J.; Willett, W.C.; Astrup, A.; Barclay, A.W.; Björck, I.; Brand-Miller, J.C.; Brighenti, F.;
Buyken, A.E. Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the
International Carbohydrate Quality Consortium (ICQC). Nutr. Metab. Cardiovasc. Dis. 2015,25, 795–815. [CrossRef]
17.
Reynolds, A.; Mann, J.; Cummings, J.; Winter, N.; Mete, E.; Te Morenga, L. Carbohydrate quality and human health: A series of
systematic reviews and meta-analyses. Lancet 2019,393, 434–445. [CrossRef]
18.
Farvid, M.S.; Homayouni, F.; Shokoohi, M.; Fallah, A.; Farvid, M.S. Glycemic index, glycemic load and their association with
glycemic control among patients with type 2 diabetes. Eur. J. Clin. Nutr. 2014,68, 459–463. [CrossRef]
19.
Livesey, G.; Taylor, R.; Livesey, H.; Liu, S. Is there a dose-response relation of dietary glycemic load to risk of type 2 diabetes?
Meta-analysis of prospective cohort studies. Am. J. Clin. Nutr. 2013,97, 584–596. [CrossRef]
20.
Schwingshackl, L.; Hoffmann, G. Long-term effects of low glycemic index/load vs. high glycemic index/load diets on parameters
of obesity and obesity-associated risks: A systematic review and meta-analysis. Nutr. Metab. Cardiovasc. Dis.
2013
,23, 699–706.
[CrossRef]
21.
Ojo, O.; Ojo, O.O.; Adebowale, F.; Wang, X.-H. The effect of dietary glycaemic index on glycaemia in patients with type 2 diabetes:
A systematic review and meta-analysis of randomized controlled trials. Nutrients 2018,10, 373. [CrossRef]
Nutrients 2023,15, 2083 12 of 14
22.
Kristo, A.S.; Matthan, N.R.; Lichtenstein, A.H. Effect of diets differing in glycemic index and glycemic load on cardiovascular risk
factors: Review of randomized controlled-feeding trials. Nutrients 2013,5, 1071–1080. [CrossRef] [PubMed]
23.
Fogelholm, M.; Larsen, T.M.; Westerterp-Plantenga, M.; Macdonald, I.; Martinez, J.A.; Boyadjieva, N.; Poppitt, S.; Schlicht,
W.; Stratton, G.; Sundvall, J.; et al. PREVIEW: Prevention of diabetes through lifestyle intervention and population studies in
Europe and around the world. design, methods, and baseline participant description of an adult cohort enrolled into a three-year
randomised clinical trial. Nutrients 2017,9, 632. [CrossRef]
24.
Zhu, R.; Larsen, T.M.; Fogelholm, M.; Poppitt, S.D.; Vestentoft, P.S.; Silvestre, M.A.-O.; Jalo, E.; Navas-Carretero, S.; Huttunen-
Lenz, M.; Taylor, M.A.; et al. Dose-Dependent Associations of Dietary Glycemic Index, Glycemic Load, and Fiber With 3-Year
Weight Loss Maintenance and Glycemic Status in a High-Risk Population: A Secondary Analysis of the Diabetes Prevention
Study PREVIEW. Diabetes Care 2021,44, 1672–1681. [CrossRef]
25.
Walton, J.A.-O.; Bell, H.; Re, R.; Nugent, A.A.-O. Current perspectives on global sugar consumption: Definitions, recommenda-
tions, population intakes, challenges and future direction. Nutr. Res. Rev. 2021, 1–22. [CrossRef]
26.
Raben, A.A.-O.; Vestentoft, P.A.-O.; Brand-Miller, J.A.-O.; Jalo, E.A.-O.; Drummen, M.A.-O.X.; Simpson, L.A.-O.; Martinez, J.A.-O.;
Handjieva-Darlenska, T.A.-O.X.; Stratton, G.A.-O.; Huttunen-Lenz, M.A.-O.; et al. The PREVIEW intervention study: Results
from a 3-year randomized 2
×
2 factorial multinational trial investigating the role of protein, glycaemic index and physical
activity for prevention of type 2 diabetes. Diabetes Obes. Metab. 2021,23, 324–337. [CrossRef] [PubMed]
27.
American Diabetes Association. 2. Classification and diagnosis of diabetes: Standards of Care in Diabetes. Diabetes Care
2017
,40,
S11–S24. [CrossRef]
28.
Wolever, T.M.; Yang, M.; Zeng, X.Y.; Atkinson, F.; Brand-Miller, J.C. Food glycemic index, as given in glycemic index tables, is
a significant determinant of glycemic responses elicited by composite breakfast meals. Am. J. Clin. Nutr.
2006
,83, 1306–1312.
[CrossRef]
29.
Louie, J.C.Y.; Flood, V.M.; Atkinson, F.S.; Barclay, A.W.; Brand-Miller, J.C. Methodology for assigning appropriate glycaemic index
values to an Australian food composition database. J. Food Compos. Anal. 2015,38, 1–6. [CrossRef]
30.
van Woudenbergh, G.J.; Kuijsten, A.; Sijbrands, E.J.; Hofman, A.; Witteman, J.C.; Feskens, E.J. Glycemic index and glycemic load
and their association with C-reactive protein and incident type 2 diabetes. J. Nutr. Metab. 2011,2011. [CrossRef]
31.
Wanselius, J.; Axelsson, C.; Moraeus, L.; Berg, C.; Mattisson, I.; Larsson, C. Procedure to Estimate Added and Free Sugars in Food
Items from the Swedish Food Composition Database Used in the National Dietary Survey Riksmaten Adolescents. Nutrients
2019
,
11, 1342. [CrossRef]
32.
Willett, W.C.; Howe, G.R.; Kushi, L.H. Adjustment for total energy intake in epidemiologic studies. Am. J. Clin. Nutr.
1997
,65,
1220S–1228S. [CrossRef]
33.
Schulze, M.B.; Schulz, M.; Heidemann, C.; Schienkiewitz, A.; Hoffmann, K.; Boeing, H. Carbohydrate intake and incidence of
type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Br. J. Nutr.
2008
,99,
1107–1116. [CrossRef] [PubMed]
34.
Villegas, R.; Liu, S.; Gao, Y.-T.; Yang, G.; Li, H.; Zheng, W.; Shu, X.O. Prospective study of dietary carbohydrates, glycemic index,
glycemic load, and incidence of type 2 diabetes mellitus in middle-aged Chinese women. Arch. Intern. Med.
2007
,167, 2310–2316.
[CrossRef] [PubMed]
35.
Janket, S.-J.; Manson, J.E.; Sesso, H.; Buring, J.E.; Liu, S. A prospective study of sugar intake and risk of type 2 diabetes in women.
Diabetes Care 2003,26, 1008–1015. [CrossRef]
36.
Ahmadi-Abhari, S.; Luben, R.N.; Powell, N.; Bhaniani, A.; Chowdhury, R.; Wareham, N.J.; Forouhi, N.G.; Khaw, K.-T. Dietary
intake of carbohydrates and risk of type 2 diabetes: The European Prospective Investigation into Cancer-Norfolk study. Br. J.
Nutr. 2014,111, 342–352. [CrossRef]
37.
Della Corte, K.A.; Penczynski, K.; Kuhnle, G.; Perrar, I.; Herder, C.; Roden, M.; Wudy, S.A.; Remer, T.; Alexy, U.; Buyken, A.E. The
prospective association of dietary sugar intake in adolescence with risk markers of type 2 diabetes in young adulthood. Front.
Nutr. 2021,7, 615684. [CrossRef]
38.
Montonen, J.; Järvinen, R.; Knekt, P.; Heliovaara, M.; Reunanen, A. Consumption of sweetened beverages and intakes of fructose
and glucose predict type 2 diabetes occurrence. J. Nutr. 2007,137, 1447–1454. [CrossRef]
39.
Wu, T.; Giovannucci, E.; Pischon, T.; Hankinson, S.E.; Ma, J.; Rifai, N.; Rimm, E.B. Fructose, glycemic load, and quantity and
quality of carbohydrate in relation to plasma C-peptide concentrations in US women. Am. J. Clin. Nutr.
2004
,80, 1043–1049.
[CrossRef]
40.
Imamura, F.; O’Connor, L.; Ye, Z.; Mursu, J.; Hayashino, Y.; Bhupathiraju, S.N.; Forouhi, N.G. Consumption of sugar sweetened
beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: Systematic review, meta-analysis,
and estimation of population attributable fraction. BMJ 2015,351, h3576. [CrossRef]
41.
Schwingshackl, L.; Hoffmann, G.; Lampousi, A.-M.; Knüppel, S.; Iqbal, K.; Schwedhelm, C.; Bechthold, A.; Schlesinger, S.; Boeing,
H. Food groups and risk of type 2 diabetes mellitus: A systematic review and meta-analysis of prospective studies. Eur. J.
Epidemiol. 2017,32, 363–375. [CrossRef] [PubMed]
42.
McKeown, N.M.; Dashti, H.S.; Ma, J.; Haslam, D.E.; Kiefte-de Jong, J.C.; Smith, C.E.; Tanaka, T.; Graff, M.; Lemaitre, R.N.; Rybin,
D. Sugar-sweetened beverage intake associations with fasting glucose and insulin concentrations are not modified by selected
genetic variants in a ChREBP-FGF21 pathway: A meta-analysis. Diabetologia 2018,61, 317–330. [CrossRef] [PubMed]
Nutrients 2023,15, 2083 13 of 14
43.
Qin, P.; Li, Q.; Zhao, Y.; Chen, Q.; Sun, X.; Liu, Y.; Li, H.; Wang, T.; Chen, X.; Zhou, Q. Sugar and artificially sweetened beverages
and risk of obesity, type 2 diabetes mellitus, hypertension, and all-cause mortality: A dose–response meta-analysis of prospective
cohort studies. Eur. J. Epidemiol. 2020,35, 655–671. [CrossRef]
44.
Colditz, G.A.; Manson, J.; Stampfer, M.J.; Rosner, B.; Willett, W.C.; Speizer, F.E. Diet and risk of clinical diabetes in women. Am. J.
Clin. Nutr. 1992,55, 1018–1023. [CrossRef] [PubMed]
45.
Feskens, E.J.; Virtanen, S.M.; Räsänen, L.; Tuomilehto, J.; Stengård, J.; Pekkanen, J.; Nissinen, A.; Kromhout, D. Dietary factors
determining diabetes and impaired glucose tolerance: A 20-year follow-up of the Finnish and Dutch cohorts of the Seven
Countries Study. Diabetes Care 1995,18, 1104–1112. [CrossRef]
46.
Barclay, A.W.; Flood, V.M.; Rochtchina, E.; Mitchell, P.; Brand-Miller, J.C. Glycemic index, dietary fiber, and risk of type 2 diabetes
in a cohort of older Australians. Diabetes Care 2007,30, 2811–2813. [CrossRef]
47.
Sluijs, I.; Van Der Schouw, Y.T.; Van Der A, D.L.; Spijkerman, A.M.; Hu, F.B.; Grobbee, D.E.; Beulens, J.W. Carbohydrate quantity
and quality and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition–Netherlands
(EPIC-NL) study. Am. J. Clin. Nutr. 2010,92, 905–911. [CrossRef]
48.
Heikkilä, H.; Schwab, U.; Krachler, B.; Männikkö, R.; Rauramaa, R. Dietary associations with prediabetic states—The DR’s EXTRA
study (ISRCTN45977199). Eur. J. Clin. Nutr. 2012,66, 819–824. [CrossRef]
49.
dden Biggelaar, L.J.; Eussen, S.J.; Sep, S.J.; Mari, A.; Ferrannini, E.; van Dongen, M.C.; Denissen, K.F.; Wijckmans, N.E.; Schram,
M.T.; van der Kallen, C.J. Associations of dietary glucose, fructose, and sucrose with
β
-cell function, insulin sensitivity, and type 2
diabetes in the Maastricht study. Nutrients 2017,9, 380. [CrossRef]
50.
Tasevska, N.; Pettinger, M.; Kipnis, V.; Midthune, D.; Tinker, L.F.; Potischman, N.; Neuhouser, M.L.; Beasley, J.M.; Van Horn,
L.; Howard, B.V. Associations of biomarker-calibrated intake of total sugars with the risk of type 2 diabetes and cardiovascular
disease in the Women’s Health Initiative Observational Study. Am. J. Epidemiol. 2018,187, 2126–2135. [CrossRef]
51.
Weber, K.S.; Simon, M.-C.; Strassburger, K.; Markgraf, D.F.; Buyken, A.E.; Szendroedi, J.; Müssig, K.; Roden, M.; Group, G.
Habitual fructose intake relates to insulin sensitivity and fatty liver index in recent-onset type 2 diabetes patients and individuals
without diabetes. Nutrients 2018,10, 774. [CrossRef]
52.
Kim, H.S.; Paik, H.Y.; Lee, K.U.; Lee, H.K.; Min, H.K. Effects of several simple sugars on serum glucose and serum fructose levels
in normal and diabetic subjects. Diabetes Res. Clin. Pract. 1988,4, 281–287. [CrossRef] [PubMed]
53.
Barclay, A.W.; Petocz, P.; McMillan-Price, J.; Flood, V.M.; Prvan, T.; Mitchell, P.; Brand-Miller, J.C. Glycemic index, glycemic load,
and chronic disease risk—A meta-analysis of observational studies. Am. J. Clin. Nutr. 2008,87, 627–637. [CrossRef] [PubMed]
54.
Du, H.; van der A, D.L.; van Bakel, M.M.; van der Kallen, C.J.; Blaak, E.E.; van Greevenbroek, M.M.; Jansen, E.H.; Nijpels, G.;
Stehouwer, C.D.; Dekker, J.M. index and glycemic load in relation to food and nutrient intake and metabolic risk factors in a
Dutch population. Am. J. Clin. Nutr. 2008,87, 655–661. [CrossRef]
55.
Bhupathiraju, S.N.; Tobias, D.K.; Malik, V.S.; Pan, A.; Hruby, A.; Manson, J.E.; Willett, W.C.; Hu, F.B. Glycemic index, glycemic
load, and risk of type 2 diabetes: Results from 3 large US cohorts and an updated meta-analysis. Am. J. Clin. Nutr.
2014
,100,
218–232. [CrossRef]
56.
Dong, J.-Y.; Zhang, L.; Zhang, Y.-H.; Qin, L.-Q. Dietary glycaemic index and glycaemic load in relation to the risk of type 2
diabetes: A meta-analysis of prospective cohort studies. Br. J. Nutr. 2011,106, 1649–1654. [CrossRef]
57.
Hardy, D.S.; Garvin, J.T.; Xu, H. Carbohydrate quality, glycemic index, glycemic load and cardiometabolic risks in the US, Europe
and Asia: A dose–response meta-analysis. Nutr. Metab. Cardiovasc. Dis. 2020,30, 853–871. [CrossRef]
58. WHO. Sugars Intake for Adults and Children Geneva; World Health Organization: Geneva, Switzerland, 2015.
59. Bray, G.A. Soft drink consumption and obesity: It is all about fructose. Curr. Opin. Lipidol. 2010,21, 51–57. [CrossRef]
60.
DiNicolantonio, J.J.; Mehta, V.; Onkaramurthy, N.; O’Keefe, J.H. Fructose-induced inflammation and increased cortisol: A new
mechanism for how sugar induces visceral adiposity. Prog. Cardiovasc. Dis. 2018,61, 3–9. [CrossRef]
61.
Goran, M.I.; Dumke, K.; Bouret, S.G.; Kayser, B.; Walker, R.W.; Blumberg, B. The obesogenic effect of high fructose exposure
during early development. Nat. Rev. Endocrinol. 2013,9, 494–500. [CrossRef]
62.
Malik, V.S.; Hu, F.B. Fructose and cardiometabolic health: What the evidence from sugar-sweetened beverages tells us. J. Am. Coll.
Cardiol. 2015,66, 1615–1624. [CrossRef] [PubMed]
63.
Stanhope, K.L. Role of fructose-containing sugars in the epidemics of obesity and metabolic syndrome. Annu. Rev. Med.
2012
,63,
329–343. [CrossRef] [PubMed]
64.
Malik, V.S.; Schulze, M.B.; Hu, F.B. Intake of sugar-sweetened beverages and weight gain: A systematic review. Am. J. Clin. Nutr.
2006,84, 274–288. [CrossRef] [PubMed]
65.
White, J.S. Challenging the fructose hypothesis: New perspectives on fructose consumption and metabolism. Adv. Nutr.
2013
,4,
246–256. [CrossRef] [PubMed]
66. Ludwig, D.S. Examining the health effects of fructose. JAMA 2013,310, 33–34. [CrossRef] [PubMed]
67.
Almiron-Roig, E.; Palla, L.; Guest, K.; Ricchiuti, C.; Vint, N.; Jebb, S.A.; Drewnowski, A. Factors that determine energy
compensation: A systematic review of preload studies. Nutr. Rev. 2013,71, 458–473. [CrossRef] [PubMed]
68.
De Castro, J.M. The effects of the spontaneous ingestion of particular foods or beverages on the meal pattern and overall nutrient
intake of humans. Physiol. Behav. 1993,53, 1133–1144. [CrossRef]
69.
Rolls, B.J.; Kim, S.; Fedoroff, I.C. Effects of drinks sweetened with sucrose or aspartame on hunger, thirst and food intake in men.
Physiol. Behav. 1990,48, 19–26. [CrossRef]
Nutrients 2023,15, 2083 14 of 14
70.
Thomas, D.; Elliott, E.J.; Baur, L. Low glycaemic index or low glycaemic load diets for overweight and obesity. Cochrane Database
Syst. Rev. 2007, CD005105. [CrossRef]
71.
Bazzano, L.A.; Li, T.Y.; Joshipura, K.J.; Hu, F.B. Intake of fruit, vegetables, and fruit juices and risk of diabetes in women. Diabetes
Care 2008,31, 1311–1317. [CrossRef]
72.
Harding, A.-H.; Wareham, N.J.; Bingham, S.A.; Khaw, K.; Luben, R.; Welch, A.; Forouhi, N.G. Plasma vitamin C level, fruit
and vegetable consumption, and the risk of new-onset type 2 diabetes mellitus: The European prospective investigation of
cancer–Norfolk prospective study. Arch. Intern. Med. 2008,168, 1493–1499. [CrossRef]
73.
Wolever, T.; Nguyen, P.-M.; Chiasson, J.-L.; Hunt, J.A.; Josse, R.G.; Palmason, C.; Rodger, N.W.; Ross, S.A.; Ryan, E.A.; Tan, M.H.
Determinants of diet glycemic index calculated retrospectively from diet records of 342 individuals with non-insulin-dependent
diabetes mellitus. Am. J. Clin. Nutr. 1994,59, 1265–1269. [CrossRef] [PubMed]
74. Buijsse, B.; Boeing, H.; Drogan, D.; Schulze, M.; Feskens, E.; Amiano, P.; Barricarte, A.; Clavel-Chapelon, F.; de Lauzon-Guillain,
B.; Fagherazzi, G. Consumption of fatty foods and incident type 2 diabetes in populations from eight European countries. Eur. J.
Clin. Nutr. 2015,69, 455–461. [CrossRef]
75.
Aune, D.; Norat, T.; Romundstad, P.; Vatten, L.J. Dairy products and the risk of type 2 diabetes: A systematic review and
dose-response meta-analysis of cohort studies. Am. J. Clin. Nutr. 2013,98, 1066–1083. [CrossRef] [PubMed]
76.
Lei, L.; Rangan, A.; Flood, V.M.; Louie, J.C. Dietary intake and food sources of added sugar in the Australian population. Br. J.
Nutr. 2016,115, 868–877. [CrossRef] [PubMed]
77.
Park, Y.; Dodd, K.W.; Kipnis, V.; Thompson, F.E.; Potischman, N.; Schoeller, D.A.; Baer, D.J.; Midthune, D.; Troiano, R.P.;
Bowles, H. Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and
food-frequency questionnaires against recovery biomarkers. Am. J. Clin. Nutr. 2018,107, 80–93. [CrossRef]
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
Content uploaded by Karen W. Della Corte
Author content
All content in this area was uploaded by Karen W. Della Corte on May 03, 2023
Content may be subject to copyright.
